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audio_data.py
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audio_data.py
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import os
import os.path
import math
import threading
import torch
import torch.utils.data
import numpy as np
import librosa as lr
import bisect
class WavenetDataset(torch.utils.data.Dataset):
def __init__(self,
dataset_file,
item_length,
target_length,
file_location=None,
classes=256,
sampling_rate=16000,
mono=True,
normalize=False,
dtype=np.uint8,
train=True,
test_stride=100):
# |----receptive_field----|
# |--output_length--|
# example: | | | | | | | | | | | | | | | | | | | | |
# target: | | | | | | | | | |
self.dataset_file = dataset_file
self._item_length = item_length
self._test_stride = test_stride
self.target_length = target_length
self.classes = classes
if not os.path.isfile(dataset_file):
assert file_location is not None, "no location for dataset files specified"
self.mono = mono
self.normalize = normalize
self.sampling_rate = sampling_rate
self.dtype = dtype
self.create_dataset(file_location, dataset_file)
else:
# Unknown parameters of the stored dataset
# TODO Can these parameters be stored, too?
self.mono = None
self.normalize = None
self.sampling_rate = None
self.dtype = None
self.data = np.load(self.dataset_file, mmap_mode='r')
self.start_samples = [0]
self._length = 0
self.calculate_length()
self.train = train
print("one hot input")
# assign every *test_stride*th item to the test set
def create_dataset(self, location, out_file):
print("create dataset from audio files at", location)
self.dataset_file = out_file
files = list_all_audio_files(location)
processed_files = []
for i, file in enumerate(files):
print(" processed " + str(i) + " of " + str(len(files)) + " files")
file_data, _ = lr.load(path=file,
sr=self.sampling_rate,
mono=self.mono)
if self.normalize:
file_data = lr.util.normalize(file_data)
quantized_data = quantize_data(file_data, self.classes).astype(self.dtype)
processed_files.append(quantized_data)
np.savez(self.dataset_file, *processed_files)
def calculate_length(self):
start_samples = [0]
for i in range(len(self.data.keys())):
start_samples.append(start_samples[-1] + len(self.data['arr_' + str(i)]))
available_length = start_samples[-1] - (self._item_length - (self.target_length - 1)) - 1
self._length = math.floor(available_length / self.target_length)
self.start_samples = start_samples
def set_item_length(self, l):
self._item_length = l
self.calculate_length()
def __getitem__(self, idx):
if self._test_stride < 2:
sample_index = idx * self.target_length
elif self.train:
sample_index = idx * self.target_length + math.floor(idx / (self._test_stride-1))
else:
sample_index = self._test_stride * (idx+1) - 1
file_index = bisect.bisect_left(self.start_samples, sample_index) - 1
if file_index < 0:
file_index = 0
if file_index + 1 >= len(self.start_samples):
print("error: sample index " + str(sample_index) + " is to high. Results in file_index " + str(file_index))
position_in_file = sample_index - self.start_samples[file_index]
end_position_in_next_file = sample_index + self._item_length + 1 - self.start_samples[file_index + 1]
if end_position_in_next_file < 0:
file_name = 'arr_' + str(file_index)
this_file = np.load(self.dataset_file, mmap_mode='r')[file_name]
sample = this_file[position_in_file:position_in_file + self._item_length + 1]
else:
# load from two files
file1 = np.load(self.dataset_file, mmap_mode='r')['arr_' + str(file_index)]
file2 = np.load(self.dataset_file, mmap_mode='r')['arr_' + str(file_index + 1)]
sample1 = file1[position_in_file:]
sample2 = file2[:end_position_in_next_file]
sample = np.concatenate((sample1, sample2))
example = torch.from_numpy(sample).type(torch.LongTensor)
one_hot = torch.FloatTensor(self.classes, self._item_length).zero_()
one_hot.scatter_(0, example[:self._item_length].unsqueeze(0), 1.)
target = example[-self.target_length:].unsqueeze(0)
return one_hot, target
def __len__(self):
test_length = math.floor(self._length / self._test_stride)
if self.train:
return self._length - test_length
else:
return test_length
def quantize_data(data, classes):
mu_x = mu_law_encoding(data, classes)
bins = np.linspace(-1, 1, classes)
quantized = np.digitize(mu_x, bins) - 1
return quantized
def list_all_audio_files(location):
audio_files = []
for dirpath, dirnames, filenames in os.walk(location):
for filename in [f for f in filenames if f.endswith((".mp3", ".wav", ".aif", "aiff"))]:
audio_files.append(os.path.join(dirpath, filename))
if len(audio_files) == 0:
print("found no audio files in " + location)
return audio_files
def mu_law_encoding(data, mu):
mu_x = np.sign(data) * np.log(1 + mu * np.abs(data)) / np.log(mu + 1)
return mu_x
def mu_law_expansion(data, mu):
s = np.sign(data) * (np.exp(np.abs(data) * np.log(mu + 1)) - 1) / mu
return s